The present invention relates generally to the field of medical imaging and particularly to the field of volumetric medical imaging. Specifically, the invention relates to a technique for segmenting bone and vasculature data in computed tomography.
Volumetric medical imaging technologies use a variety of techniques to gather three-dimensional information about the body. For example, computed tomography (CT) imaging system measure the attenuation of X-ray beams passed through a patient from numerous angles. Based upon these measurements, a computer is able to reconstruct images of the portions of a patient's body responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are based upon separate examination of a series of angularly displaced cross sections. It should be pointed out that a CT system produces data that represent the distribution of linear attenuation coefficients of the scanned object. The data are then reconstructed to produce an image that is typically displayed on a cathode ray tube, and may be printed or reproduced on film.
For example, in the field of CT angiography (CTA), vasculature and other circulatory system structures may be imaged, typically by administration of a radio-opaque dye prior to imaging. Visualization of the CTA data typically is performed in a two-dimensional manner, i.e., slice-by-slice, or in a three-dimensional manner, i.e., volume visualization, which allows the data to be analyzed for vascular pathologies. For example, the data may be analyzed for aneurysms, vascular calcification, renal donor assessment, stent placement, vascular blockage, and vascular evaluation for sizing and/or runoff. Once a pathology is located, quantitative assessments of the pathology may be made of the on the original two-dimensional slices.
The CTA process may include processes for segmenting structures in the image data, such as the vasculature and/or the bone structures. Such segmentation typically involves identifying which voxels of the image data are associated with a particular structure or structures of interest. Segmented structures may then be viewed outside of the context of the remainder of the image data or may be masked from the remainder of the image data to allow otherwise obstructed structure to be viewed. For example, in CTA, segmentation may be performed to identify all voxels associated with the vasculature, allowing the entire circulatory system in the imaged region to be extracted and viewed. Similarly, all voxels of the bone structures may be identified and masked, or subtracted, from the image data, allowing vasculature and/or other structures which might otherwise be obscured by the relatively opaque bone structures to be observed during subsequent visualization.
However, segmentation of vasculature and bone structures may be complicated by a variety of factors. For example, in CTA, overlapping image intensities, close proximity of imaged structures, limited detector resolution, calcification, and interventional devices may make the identification and segmentation of bone and vascular structures difficult. Furthermore, anatomic regions and sub-regions of complex anatomy within the imaging volume may benefit from differential processing techniques. In particular, the complex landscape of the bone and vasculature in the head and neck region, may benefit from differential processing based on distinct sub-regions within the overall regions.
Because of these complicating factors, existing segmentation techniques may improperly exclude image data from the segmented structure due to poor edge recognition and/or non-homogeneities in the image data. Such exclusions may potentially result in early or erroneous termination of the segmentation technique. Furthermore, splits or mergers in the structure of interest may not be properly segmented by existing techniques due to these shortcomings. Alternatively, overlapping intensity ranges and/or poor edge recognition may result in the improper inclusion of proximate background regions in the segmented structure, leading to late termination of the segmentation process.
As a result, proper segmentation of a complex or contiguous three-dimensional structure, such as the vasculature around the head and neck region, may require operator intervention or input. In particular, operator intervention may be needed to designate initial start points and/or to prevent the inadvertent inclusion or exclusions of volume data from the segmented structure. For example, the operator may manually remove bone by drawing contours around bone on a few slices and building up the bone structure to be removed. Based on the results, the operator may repeat this process for the slices in question until a satisfactory result in achieved, at which time the operator may proceed to the next set of slices to be iteratively processed in this manner. This process may be particularly difficult in the head and neck region due to the poor bone boundaries observed via CT and other imaging modalities. Furthermore, the shape of the bone may change rapidly in the space of a few slices, preventing the operator from using the same contour as an initial reference in more than a few slices. As a result, the operator may have to redraw or reset the contour repeatedly throughout the process, potentially taking over an hour to process an imaged head and neck volume. In addition, the operator intervention may lead to inter- and intra-user variability in the segmentation of structures.
Due to the labor intensiveness of this process, the operator may attempt to reduce the processing time by limiting the process to a region or volume of interest corresponding to the location of the pathology. Limiting the process in this manner, however, does not take full advantage of the entire set of volume data available. Furthermore, limiting the processing to the known or suspected region of a pathology may prevent additional pathologies which are obscured from being discovered, despite the availability of the data. It may, therefore, be desirable to automate the process for segmentation, extraction and masking of structure in the CTA process for complex anatomic regions, such as the head and neck.